Living bacterial reservoir computers for information processing and sensing

We introduce a systems-level approach to sensing and computing in which Escherichia coli acts as a living reservoir computer, performing complex information processing through its native growth responses without requiring genetic modification or specialized instrumentation. We validate this framework by accurately classifying early-stage COVID-19 plasma samples according to subsequent disease severity using only bacterial growth data, highlighting its prognostic potential without the need for infrastructure-dependent methods. By controlling nutrient media compositions, we also demonstrate that E. coli growth encodes nonlinear transformations that outperform linear regression, support vector machines, and multilayer perceptrons across diverse regression and classification tasks. More broadly, simulations across genome-scale metabolic models from multiple bacterial species support a link between phenotypic diversity and computational capacity. These findings position biological reservoir computing as a robust, scalable, and low-cost platform for intelligent biosensing, diagnostics, and hybrid bio-digital computation, while providing new mechanistic insights into the computational capabilities of living systems.